{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QRS peak detection\n",
    "Detection of cardiac cycles from ECG signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup\n",
    "import sys\n",
    "import numpy as np\n",
    "import scipy.signal as sp\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import wfdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download records\n",
    "Identify and download records in the MIMIC III Waveform Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected record: 3000063_0013\n",
      "10 seconds of data loaded from: 3000063_0013\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the first record\n",
    "selected_record = '3000063_0013'\n",
    "database_name = 'mimic3wdb/1.0/30/3000063/'\n",
    "print(\"Selected record: {}\".format(selected_record))\n",
    "\n",
    "# load data from this record\n",
    "start_seconds = 20\n",
    "no_seconds_to_load = 10\n",
    "fs = 125\n",
    "record_data = wfdb.rdrecord(record_name = selected_record, sampfrom = fs*start_seconds, sampto = fs*(start_seconds + no_seconds_to_load), pn_dir = database_name) \n",
    "print(\"{} seconds of data loaded from: {}\".format(no_seconds_to_load, selected_record))\n",
    "\n",
    "# Plot the data loaded from this record\n",
    "title_text = \"First record from \" + database_name + \" (record: \" + selected_record + \")\"\n",
    "wfdb.plot_wfdb(record=record_data, title=title_text, time_units='seconds') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separate records\n",
    "Separate ECG, PPG and ABP signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG at position 0\n",
      "ABP at position 1\n",
      "PPG at position 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Time [s]'), Text(0, 0.5, 'ABP')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ECG, PPG and ABP extraction\n",
    "h = 0\n",
    "for i in record_data.sig_name:\n",
    "    if i == 'II':\n",
    "        print('ECG at position ' + str(h))\n",
    "        ecg = record_data.p_signal[:,h]\n",
    "    elif i == 'PLETH':\n",
    "        print('PPG at position ' + str(h))\n",
    "        ppg = record_data.p_signal[:,h]\n",
    "    elif i == 'ABP':\n",
    "        print('ABP at position ' + str(h))\n",
    "        abp = record_data.p_signal[:,h]\n",
    "    else:\n",
    "        print('Other signal (' + i + ') at position ' + str(h))\n",
    "    h = h + 1\n",
    "\n",
    "t = np.arange(start_seconds,(start_seconds + no_seconds_to_load),1.0/fs)\n",
    "\n",
    "fig, (ax1,ax2,ax3) = plt.subplots(3, 1, sharex = True, sharey = False)\n",
    "ax1.plot(t, ecg)\n",
    "ax1.set(xlabel = '', ylabel = 'ECG')\n",
    "ax2.plot(t, ppg)\n",
    "ax2.set(xlabel = '', ylabel = 'PPG')\n",
    "ax3.plot(t, abp)\n",
    "ax3.set(xlabel = 'Time [s]', ylabel = 'ABP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove NaNs\n",
    "x, = np.where(np.isnan(ecg))\n",
    "if len(x) != 0:\n",
    "    print('NaNs in ECG: ' + str(x))\n",
    "    for i in x:\n",
    "        ecg[i] = ecg[i - 1]\n",
    "x, = np.where(np.isnan(ppg))\n",
    "if len(x) != 0:\n",
    "    print('NaNs in PPG: ' + str(x))\n",
    "    for i in x:\n",
    "        ppg[i] = ppg[i - 1]\n",
    "x, = np.where(np.isnan(abp))\n",
    "if len(x) != 0:\n",
    "    print('NaNs in ABP: ' + str(x))\n",
    "    for i in x:\n",
    "        abp[i] = abp[i - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter ECG\n",
    "sos_ecg = sp.butter(10, [0.5, 40], btype = 'bp', analog = False, output = 'sos', fs = fs)\n",
    "ecg_ff = sp.sosfiltfilt(sos_ecg, ecg)\n",
    "\n",
    "# Filter PPG\n",
    "sos_ppg = sp.butter(10, [0.2, 10], btype = 'bp', analog = False, output = 'sos', fs = fs)\n",
    "ppg_ff = sp.sosfiltfilt(sos_ppg, ppg)\n",
    "\n",
    "# Filter ABP\n",
    "sos_abp = sp.butter(10, 10, btype = 'low', analog = False, output = 'sos', fs = fs)\n",
    "abp_ff = sp.sosfiltfilt(sos_abp, abp)\n",
    "\n",
    "fig, (ax1,ax2,ax3) = plt.subplots(3, 1, sharex = False, sharey = False)\n",
    "fig.suptitle('Filtered signals') \n",
    "\n",
    "ax1.plot(t, ecg, color = 'red')\n",
    "ax1.plot(t, ecg_ff, color = 'orange')\n",
    "ax1.set_xlabel('Time [s]')\n",
    "ax1.set_ylabel('ECG [V]')\n",
    "\n",
    "ax2.plot(t, ppg, color = 'red')\n",
    "ax2.plot(t, ppg_ff, color = 'orange')\n",
    "ax2.set_xlabel('Time [s]')\n",
    "ax2.set_ylabel('PPG [V]')\n",
    "\n",
    "ax3.plot(t, abp, color = 'red')\n",
    "ax3.plot(t, abp_ff, color = 'orange')\n",
    "ax3.set_xlabel('Time [s]')\n",
    "ax3.set_ylabel('ABP [V]')\n",
    "\n",
    "plt.subplots_adjust(left = 0.1,\n",
    "                    bottom = 0.1, \n",
    "                    right = 0.9, \n",
    "                    top = 0.9, \n",
    "                    wspace = 0.4, \n",
    "                    hspace = 0.4)\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QRS detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define QRS detector functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QRS detection function\n",
    "def qrs_detect(ecg,fs,w):\n",
    "    \"\"\"\n",
    "    \n",
    "    Description: QRS peak detection and correction\n",
    "    Inputs:  ecg, array with ECG signal [user defined units]\n",
    "             fs, sampling rate of signal [Hz]\n",
    "             w, window length for analysis [s]\n",
    "    Outputs: qrs, positions of R peaks [number of samples]\n",
    "             n_int, number of intervals of length w in the signal\n",
    "    \n",
    "    Libraries: NumPy (as np), SciPy (Signal, as sp), Matplotlib (PyPlot, as plt)\n",
    "    \n",
    "    Version: 1.0 - June 2022\n",
    "    \n",
    "    Developed by: Elisa Mejía-Mejía\n",
    "                   City, University of London\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Normalization of ECG\n",
    "    ecg_n = (ecg - np.min(ecg))/(np.max(ecg) - np.min(ecg))\n",
    "    ecg_n = ecg_n - np.mean(ecg_n)\n",
    "    \n",
    "    # Peak detection in windows of length w\n",
    "    n_int = np.floor(len(ecg)/(w*fs))\n",
    "    for i in range(int(n_int)):\n",
    "        start = i*fs*w\n",
    "        stop = (i + 1)*fs*w - 1\n",
    "        #print('Start: ' + str(start) + ', stop: ' + str(stop))\n",
    "        aux = ecg_n[range(start,stop)]\n",
    "        locs, amps, nlocs = rpeakdetect(aux,fs,0.2)\n",
    "        locs = locs + start\n",
    "        if i == 0:\n",
    "            qrs = locs\n",
    "        else:\n",
    "            qrs = np.append(qrs,locs)\n",
    "    if n_int*fs*w != len(ecg_n):\n",
    "        start = stop + 1\n",
    "        stop = len(ecg_n)\n",
    "        aux = ecg_n[range(start,stop)]\n",
    "        if len(aux) > 20:\n",
    "            locs, amps, nlocs = rpeakdetect(aux,fs,0.2)\n",
    "            locs = locs + start\n",
    "            qrs = np.append(qrs,locs)            \n",
    "    \n",
    "    # Peak correction\n",
    "    # (1) Not peaks\n",
    "    med_rr = np.median(np.diff(qrs))\n",
    "    med_amp = np.median(ecg[qrs])\n",
    "    pos_for = 0\n",
    "    pos_rev = 0\n",
    "    for i in range(len(qrs)):\n",
    "        cur_amp = ecg[qrs[i]]\n",
    "        if i == 1:                 # Peak is at the first position of the signal\n",
    "            next_amp = ecg[qrs[i] + 1]\n",
    "            cond = cur_amp >= next_amp\n",
    "            #print(cur_amp, next_amp, cond)\n",
    "        elif i == len(ecg):        # Peak is at the last position of the signal\n",
    "            prev_amp = ecg[qrs[i] - 1]\n",
    "            cond = cur_amp >= prev_amp\n",
    "            #print(cur_amp, prev_amp, cond)\n",
    "        else:   \n",
    "            next_amp = ecg[qrs[i] + 1]\n",
    "            prev_amp = ecg[qrs[i] - 1]\n",
    "            cond = cur_amp >= prev_amp and cur_amp >= next_amp \n",
    "            #print(cur_amp, prev_amp, next_amp, cond)    \n",
    "        \n",
    "        if not(cond):\n",
    "            # Forward search\n",
    "            j = qrs[i] + 1\n",
    "            if i == len(qrs) - 1:\n",
    "                stop = len(ecg)\n",
    "            else:\n",
    "                stop = qrs[i + 1]\n",
    "            while j < stop:\n",
    "                cur_amp = ecg[j]\n",
    "                next_amp = ecg[j + 1]\n",
    "                prev_amp = ecg[j - 1]\n",
    "                if not(cur_amp > next_amp and cur_amp > prev_amp):\n",
    "                    j = j + 1\n",
    "                else:\n",
    "                    if ecg[j] >= 0.5*med_amp:\n",
    "                        pos_for = j\n",
    "                        j = stop\n",
    "                    else:\n",
    "                        j = j + 1\n",
    "            # Reverse search\n",
    "            j = qrs[i] - 1\n",
    "            if i == 0:\n",
    "                stop = 1\n",
    "            else:\n",
    "                stop = qrs[i - 1]\n",
    "            while j > stop:\n",
    "                cur_amp = ecg[j]\n",
    "                next_amp = ecg[j + 1]\n",
    "                prev_amp = ecg[j - 1]\n",
    "                if not(cur_amp > next_amp and cur_amp > prev_amp):\n",
    "                    j = j - 1\n",
    "                else:\n",
    "                    if ecg[j] >= 0.5*med_amp:\n",
    "                        pos_rev = j\n",
    "                        j = stop\n",
    "                    else:\n",
    "                        j = j - 1\n",
    "            # Selection of peak\n",
    "            if pos_for != 0 and pos_rev != 0:\n",
    "                pos = [pos_for, pos_rev]\n",
    "                if i - 1 == 0:\n",
    "                    dif = - pos\n",
    "                else:\n",
    "                    dif = qrs[i - 1] - pos\n",
    "                dif_med = np.abs(dif - med_rr)\n",
    "                #print(dif_med)\n",
    "                min_dif = np.min(dif_med)\n",
    "                min_dif, = np.where(dif_med == min_dif)\n",
    "                #print(min_dif,pos(min_ind))\n",
    "                qrs[i] = pos(min_ind)\n",
    "    \n",
    "    # (2) Low peaks\n",
    "    med_amp = np.median(ecg[qrs])\n",
    "    #print(len(qrs))\n",
    "    result, = np.where(ecg[qrs] < 0.5*med_amp)\n",
    "    if len(result) > 0:\n",
    "        #print(result)\n",
    "        qrs = np.delete(np.array(qrs),result)\n",
    "        #print(len(qrs)) \n",
    "    # (3) Too close or too far\n",
    "    rr = np.diff(qrs)\n",
    "    avg_rr = np.mean(rr)\n",
    "    med_rr = np.median(rr)\n",
    "    q3_rr, q1_rr = np.percentile(rr,[75, 25])\n",
    "    iqr_rr = q3_rr - q1_rr\n",
    "    #print(rr, avg_rr, med_rr, q3_rr, q1_rr, iqr_rr)\n",
    "    if (med_rr < 0.5*fs or avg_rr < 0.5*fs) and (iqr_rr > 0.2*fs):\n",
    "        result, = np.where(rr <= med_rr)\n",
    "        qrs = np.delete(qrs, result)\n",
    "        med_rr = np.median(rr)\n",
    "    #   (a) Too close\n",
    "    ind, = np.where(rr < 0.5*med_rr)\n",
    "    if len(ind) != 0:\n",
    "        i = 1\n",
    "        while i <= len(ind):\n",
    "            ind = no.sort(np.append(ind, ind[i] + 1))\n",
    "            #print(ind)\n",
    "            i = i + 2\n",
    "        qrs = np.delete(qrs, ind)\n",
    "    #   (b) Too far\n",
    "    rr = np.diff(qrs)\n",
    "    ind, = np.where(rr > 1.5*med_rr)\n",
    "    th_amp = 0.5*np.median(ecg[qrs])\n",
    "    #print(ind, th_amp)   \n",
    "    if len(ind) != 0:\n",
    "        i = 0\n",
    "        while len(ind) != 0:\n",
    "            #ind = np.round(ind)\n",
    "            #qrs = np.round(qrs)\n",
    "            #print(i, ind[i], len(qrs))\n",
    "            if qrs[ind[i]] == qrs[-1]:\n",
    "                aux = ecg[qrs[ind[i]]:len(ecg)]\n",
    "            else:\n",
    "                aux = ecg[qrs[ind[i]]:qrs[ind[i] + 1]]\n",
    "            locs, _ = sp.find_peaks(aux)\n",
    "            ind_del, = np.where(aux[locs] < th_amp)\n",
    "            locs = np.delete(locs, ind_del)\n",
    "            if len(locs) == 0:\n",
    "                new = med_rr + qrs[ind[i]] - 1\n",
    "            elif len(locs) == 1:\n",
    "                new = locs + qrs[ind[i]] - 1\n",
    "            else:\n",
    "                aux_dif = np.abs(locs - med_rr)\n",
    "                min_dif = np.min(aux_dif)\n",
    "                ind_new, = np.where(aux_dif == min_dif)\n",
    "                new = locs[ind_new] + qrs[ind[i]] - 1\n",
    "            qrs = np.sort(np.append(qrs, new))\n",
    "            rr = np.diff(qrs)\n",
    "            ind, = np.where(rr > 1.5*med_rr)\n",
    "    rr = np.diff(qrs)\n",
    "    ind_del, = np.where(rr < 0.3*fs)\n",
    "    rr = np.delete(rr, ind_del)\n",
    "    \n",
    "    #fig = plt.figure()\n",
    "    #plt.plot(ecg, color = 'black')\n",
    "    #plt.scatter(qrs,ecg[qrs],marker = 'o',color = 'red')\n",
    "        \n",
    "    return qrs, n_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R peak detect\n",
    "def rpeakdetect(ecg, fs, th):\n",
    "    \"\"\"\n",
    "    \n",
    "    Description: QRS peak detection based on rpeakdetect2.m by G. Clifford\n",
    "                 A batch QRS detector based upon that of Pan, Hamilton and Tompkins:\n",
    "                 J. Pan & W. Tompkins. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, vol. BME-32 NO. 3. 1985.\n",
    "                 P. Hamilton & W. Tompkins. Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrythmia \n",
    "                 Database. IEEE Trans Biomed Eng, vol. BME-33, NO. 12.1986.\n",
    "    Inputs:  ecg, array with ECG signal [user defined units]\n",
    "             fs, sampling rate of signal [Hz]\n",
    "             th, threshold for peaks in integrated waveform - Default: 0.2\n",
    "    Outputs: locs, positions of R peaks [number of samples]\n",
    "             amps, amplitudes of R peaks [user defined units]\n",
    "             nlocs, number of R peaks found\n",
    "    \n",
    "    Libraries: NumPy (as np), SciPy (Signal, as sp), Matplotlib (PyPlot, as plt)\n",
    "    \n",
    "    Version: 1.0 - June 2022\n",
    "    \n",
    "    Developed by: Elisa Mejía-Mejía\n",
    "                  City, University of London\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Create time array\n",
    "    t = np.divide(range(0,len(ecg) - 1), fs)\n",
    "    \n",
    "    # Preprocessing:\n",
    "    # (1) Filter data\n",
    "    sos = sp.butter(6, 40, btype = 'low', analog = False, output = 'sos', fs = fs)\n",
    "    ecg_f = sp.sosfiltfilt(sos, ecg)\n",
    "    # (2) Differentiate ECG\n",
    "    ecg_d = np.diff(ecg_f)\n",
    "    # (3) Square ECG\n",
    "    ecg_s = ecg_d*ecg_d\n",
    "    # (4) Integrate data\n",
    "    if fs >= 256:\n",
    "        d = np.ones(int(np.round(7*fs/256)))\n",
    "    else:\n",
    "        d = np.ones(21)\n",
    "    ecg_fir = sp.lfilter(d, 1, ecg_s)\n",
    "    ecg_med = sp.medfilt(ecg_fir, kernel_size = 9)\n",
    "    \n",
    "    # Remove filter delay:\n",
    "    delay = np.ceil(len(d)/2)\n",
    "    ecg_med = ecg_med[int(delay):len(ecg_med)]\n",
    "    \n",
    "    # Find peaks: \n",
    "    # (1) Find highest bumps in data\n",
    "    start_int = round((len(ecg) - 1)/4)\n",
    "    stop_int = round(3*(len(ecg) - 1)/4)\n",
    "    max_h = np.max(ecg_med[start_int:stop_int])\n",
    "    # (2) Determine left and right boundaries\n",
    "    ecg_med = np.insert(ecg_med,0,0)\n",
    "    ecg_med = np.append(ecg_med,0)\n",
    "    n_left = 0\n",
    "    n_right = 0\n",
    "    for i in range(len(ecg_med)):\n",
    "        if i > 0 and i < len(ecg_med):\n",
    "            if ecg_med[i] > (th*max_h) and ecg_med[i - 1] < (th*max_h): # left boundary\n",
    "                if n_left == 0:\n",
    "                    left_bound = i - 1\n",
    "                    n_left = 1\n",
    "                else:\n",
    "                    left_bound = np.append(left_bound,i - 1)\n",
    "            if ecg_med[i] > (th*max_h) and ecg_med[i + 1] < (th*max_h): # right boundary\n",
    "                if n_right == 0:\n",
    "                    right_bound = i - 1\n",
    "                    n_right = 1\n",
    "                else:\n",
    "                    right_bound = np.append(right_bound,i - 1)        \n",
    "    #print(left_bound)\n",
    "    #print(right_bound)\n",
    "    # (4) Look through all possibilities\n",
    "    if left_bound[0] > right_bound[0]:\n",
    "        right_bound = np.delete(right_bound,0)\n",
    "    if left_bound[-1] > right_bound[-1]:\n",
    "        left_bound = np.delete(left_bound,0)\n",
    "    nlocs = 0\n",
    "    for i in range(len(left_bound)):\n",
    "        #print(i)\n",
    "        lb = left_bound[i]\n",
    "        rb, = np.where(np.array(right_bound) > lb)\n",
    "        if len(rb) != 0:\n",
    "            rb = right_bound[rb[0]]\n",
    "            #print(lb, rb)\n",
    "            \n",
    "            amp = np.max(ecg[lb:rb])\n",
    "            pos, = np.where(np.array(ecg[lb:rb]) == np.amax(ecg[lb:rb]))\n",
    "            pos = pos[0]\n",
    "            pos = int(pos + lb)\n",
    "            #print(pos, amp)\n",
    "            if nlocs == 0:\n",
    "                locs = pos\n",
    "                amps = ecg[pos]\n",
    "            else:\n",
    "                locs = np.append(locs, pos)\n",
    "                amps = np.append(amps, ecg[pos])\n",
    "            nlocs = nlocs + 1\n",
    "            #print(locs)\n",
    "    \n",
    "    #fig = plt.figure()\n",
    "    #plt.plot(ecg)\n",
    "    #plt.plot(ecg_f)\n",
    "    #plt.plot(ecg_d)\n",
    "    #plt.plot(ecg_s)\n",
    "    #plt.plot(ecg_fir)\n",
    "    #plt.plot(ecg_med)\n",
    "    #plt.scatter(locs, amps, marker = 'o')\n",
    "    \n",
    "    return locs, amps, nlocs,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd635e46f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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   "source": [
    "# Detect cardiac cycles\n",
    "qrs, n_int = qrs_detect(ecg_ff,fs,5)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.title('QRS detection') \n",
    "plt.plot(t, ecg_ff, color = 'black')\n",
    "plt.scatter(start_seconds + qrs/fs, ecg_ff[qrs], color = 'red', marker = 'o')"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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